CMMS & EAM Data Quality Challenges

International competition and market globalization place enormous pressure on corporations to improve efficiency and reliability while reducing operating costs. Corporations embrace new tools and methodologies to accelerate asset performance and maximize use of their existing resources in an attempt to become world leaders and best-in-class facilities.

Many executives acknowledge that their ability to achieve best-in-class performance and profitability is determined by how they manage and maintain their assets and production facilities. Asset-intensive organizations invest billions of dollars worldwide in ERP, EAM, and CMMS systems such as IBM Maximo, SAP PM, Oracle eAM, Infor EAM, and others to meet their maintenance and reliability goals. And yet, many load legacy data into the new system, failing to ensure their data is accurate which results in CMMS and EAM data quality challenges.

Without an adequate data foundation, CMMS and EAM systems will fail to deliver the expected value: It is simply not possible to be strategic, and continuously improve maintenance operations without assurance that decisions are based on data that is valid and fit for the purpose. Poor maintenance strategies directly impact day-to-day business and are usually a result of CMMS and EAM Data Quality Issues.

How organizations are affected by poor quality EAM and CMMS data